Bone age estimation method and apparatus

ABSTRACT

Disclosed are a bone age estimation method and a bone age estimation apparatus. The bone age estimation method may comprise the steps of: extracting a region of interest including a cervical spine region from a lateral cephalometric radiographic image obtained by imaging a subject&#39;s cervical spine, by using a first deep learning model; extracting landmarks from the extracted region of interest by using a second deep learning model; calculating a landmark numerical value on the basis of the extracted landmarks; and providing maturity information of a maturation stage of the cervical spine on the basis of the calculated landmark numerical value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. national stage entry of InternationalApplication No. PCT/KR2020/018320, filed Dec. 14, 2020, which claimspriority to Korean Application No. 10-2020-0009524, filed Jan. 23, 2020,and Korean Application No. 10-2020-0066130, filed Jun. 2, 2020, theentire disclosures of which are incorporated herein by reference

TECHNICAL FIELD

The following description relates to a bone age estimation technique.

BACKGROUND

In the field of orthodontics, deep learning based diagnostic assistanceprograms which mark anatomical landmarks limited to the upper jaw, thelower jaw, and skull and automatically perform orthodontic analysisthereby are currently released in the related art. However, the relatedart has a characteristic in that it is limited to diagnosis oforthodontics problems of the patient in the current state. Due to thischaracteristic, the related art has a limitation that it may not beapplied to a technique of predicting a growing stage of the patient.Accordingly, there is a need for research to supplement the limitationof the related art.

SUMMARY

According to an aspect, a bone age estimation method may includeextracting a region of interest including a cervical spine region from alateral cephalometric radiographic image obtained by imaging a subject'scervical spine, by using a first deep learning model; extractinglandmarks from the extracted region of interest by using a second deeplearning model; calculating a landmark numerical value on the basis ofthe extracted landmarks; and providing maturity information of amaturation stage of the cervical spine on the basis of the calculatedlandmark numerical value.

The step of calculating a landmark numerical value may include: a stepof calculating a landmark numerical value including at least one of aratio of a height to a length of the cervical spine lower border, aratio of a height to a length of the cervical spine lower border thatdraws a vertical line, and a concavity ratio of the cervical spine lowerborder, on the basis of the coordinates of the landmarks.

The step of extracting landmarks may include: a step of changing acoordinate of the extracted landmarks on the basis of the user inputwhen a user input to change the extracted landmark is received.

The bone age estimation method according to the example embodiment mayfurther include training the second deep learning model on the basis ofthe changed coordinate when the coordinate of the extracted landmark ischanged by the user input.

The step of providing maturity information may include: a step ofvisually providing the maturity information of the cervical spine on thebasis of at least one of a chronological age of the subject, a gender ofthe subject, age-specific standardization information for a concavity ofthe fourth cervical spine lower border corresponding to the gender ofthe subject, and a concavity ratio of the fourth cervical spine lowerborder of the subject.

In the step of providing maturity information, at least one of a graphcorresponding to the standardization information, a graph correspondingto the concavity ratio of the fourth cervical spine lower border of thesubject, and a graph corresponding to the chronological age of thesubject may be provided to be overlaid.

The bone age estimation method according to the example embodiment mayfurther include a step of adjusting a brightness of the region ofinterest and reversing left and right of the region of interest afterextracting the region of interest.

The step of extracting landmarks may include: a step of providing anumerical value obtained by calculating a progress degree that thelandmarks are extracted by a percentage, on the basis of a predeterminedperiod.

The region of interest may include at least one of a second cervicalspine, a third cervical spine, and a fourth cervical spine of thesubject.

Each of the extracted landmarks is assigned with a tracking number onthe basis of a position of each of the landmarks and the landmarks andthe tracking numbers corresponding to the landmarks may be displayed inthe region of interest.

It may be determined whether to display the extracted landmarks on thebasis of a user input for selecting a landmark to be displayed.

The landmarks may be points obtained by anatomically measuring andmarking at least one of the second cervical spine, the third cervicalspine, and the fourth cervical spine of the subject.

According to another aspect, a bone age estimation apparatus whichperforms a bone age estimation method includes a memory and a processor,the memory stores instructions executable by the processor, and when theinstructions are executed by the processor, the processor may controlthe bone age estimation apparatus to allow the bone age estimationapparatus to extract a region of interest including a cervical spineregion from a lateral cephalometric radiographic image obtained byimaging a subject's cervical spine, by using a first deep learningmodel, extract landmarks from the extracted region of interest by usinga second deep learning model, calculate a landmark numerical value onthe basis of the extracted landmarks, and provide maturity informationof a maturation stage of the cervical spine on the basis of thecalculated landmark numerical value.

The processor may control the bone age estimation apparatus to allow thebone age estimation apparatus to calculate a landmark numerical valueincluding at least one of a ratio of a height to a length of thecervical spine lower border, a ratio of a height to a length of thecervical spine lower border that draws a vertical line, and a concavityratio of the cervical spine lower border, on the basis of thecoordinates of the landmarks.

The processor may control the bone age estimation apparatus to allow thebone age estimation apparatus to change a coordinate of the extractedlandmarks on the basis of the user input when a user input to change theextracted landmark is received.

The processor may control the bone age estimation apparatus to allow thebone age estimation apparatus to train the second deep learning model onthe basis of the changed coordinate when the coordinate of the extractedlandmark is changed by the user input.

The processor may control the bone age estimation apparatus to allow thebone age estimation apparatus to visually provide the maturityinformation of the cervical spine on the basis of at least one of achronological age of the subject, a gender of the subject, age-specificstandardization information for a concavity of the fourth cervical spinelower border corresponding to the gender of the subject, and a concavityratio of the fourth cervical spine lower border of the subject.

The processor may control the bone age estimation apparatus to allow thebone age estimation apparatus to provide at least one of a graphcorresponding to the standardization information, a graph correspondingto the concavity ratio of the fourth cervical spine lower border of thesubject, and a graph corresponding to the chronological age of thesubject to be overlaid.

The landmarks may be points obtained by anatomically measuring andmarking at least one of the second cervical spine, the third cervicalspine, and the fourth cervical spine of the subject.

According to the example embodiment, a deep learning based program whichpredicts a growth of a skeleton through a lateral cephalometricradiographic image may be provided.

According to the example embodiment, a cervical spine region isautomatically cropped from the lateral cephalometric radiographic imageand anatomical landmarks of the cervical spine are automatically marked.

According to the example embodiment, a bone age of the cervical spinemay be analyzed on the basis of the anatomical landmarks of the cervicalspine which are automatically marked.

According to the example embodiment, an average cervical spineevaluation index of growing patients in Korea is provided on a graph tointuitively present the degree of distribution of specific patients onthe table.

According to the example embodiment, problems of the related art such asinconvenience of taking hand skeleton radiographic images, additionalcosts, and the increase in the amount of radiation exposed to thepatients of children and adolescents caused to predict the growth of thechildren and adolescents may be solved.

According to the example embodiment, the landmarks are automaticallymarked to shorten an amount of labor and times required for a clinicianto make a diagnosis.

According to the example embodiment, the landmark numerical values areprovided on one screen to allow the user to intuitively andcomprehensively check the diagnostic result.

According to the example embodiment, the growth of orthodontic patientsmay be predicted.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flowchart for explaining a bone age estimation methodaccording to an example embodiment.

FIGS. 2A to 2C are views illustrating examples of an interface in whicha bone age estimation method according to an example embodiment isperformed.

FIG. 3 is a view illustrating a bone age estimation apparatus accordingto an example embodiment.

DETAILED DESCRIPTION

Hereinafter, example embodiments will be described in detail withreference to the accompanying drawings. However, various changes may beapplied to the example embodiments so that the scope of the applicationis not restricted or limited by the example embodiments. It should beunderstood that all changes, equivalents, or substitutes of exampleembodiments are included in the scope of the rights.

Terms used in the example embodiment are used only for illustrativepurposes only, but should not be interpreted as an intention to limitthe invention. A singular form may include a plural form if there is noclearly opposite meaning in the context. In the present specification,it should be understood that terminology “include” or “have” indicatesthat a feature, a number, a step, an operation, a component, a part orthe combination thereof described in the specification is present, butdo not exclude a possibility of presence or addition of one or moreother features, numbers, steps, operations, components, parts orcombinations, in advance.

If it is not contrarily defined, all terms used herein includingtechnological or scientific terms have the same meaning as thosegenerally understood by a person with ordinary skill in the art. Termsdefined in generally used dictionary shall be construed that they havemeanings matching those in the context of a related art, and shall notbe construed in ideal or excessively formal meanings unless they areclearly defined in the present application.

In addition, in description with reference to accompanying drawings, thesame components are denoted by the same reference numerals regardless ofthe reference numeral and a duplicated description thereof will beomitted. In description of an example embodiment, if it is determinedthat detailed description for a related art may unnecessarily blur thegist of the example embodiment, the detailed description will beomitted.

Further, in the description of the components of the example embodiment,a terminology such as a first, a second, A, B, (a), (b) may be used.However, such terminologies are used only to distinguish a componentfrom another component but nature, a sequence or an order of thecomponent is not limited by the terminologies. If it is described that acomponent is “connected” or “coupled” to another component, it isunderstood that the component is directly connected or coupled to theother component but another component may be “connected” or “coupled”between the components.

A component including a common function to a component included in anyone example embodiment will be described with the same title in anotherexample embodiment. If it is not contrarily defined, description of anyone example embodiment may be applied to another example embodiment anda detailed description of overlapping parts will be omitted.

FIG. 1 is a flowchart for explaining a bone age estimating methodaccording to an example embodiment.

A bone age estimation method described in the present specification maybe disclosed by means of a program which assists the diagnosis of theradiographic image on the basis of artificial intelligence. The bone ageestimation method may provide a method of dividing a skull and a facialbone of a lateral cephalometric radiographic image of the facial boneimaged for orthodontic diagnosis in the dentistry and automaticallyrecognizing and marking anatomical landmarks such as a cervical spine.According to the bone age estimation method, a maturation stage of acervical spine of a subject (or a patient) may be predicted using therecognized skull, facial bone, and landmarks. The bone age estimationmethod may be provided by a marking assistance program which analyzes anaspect of development of bones such as cervical spine and proposes abone age required to grow the skeleton of the subject. The bone ageestimation method may provide a method that helps to predict the growthof the orthodontic patient. The bone age estimation apparatus which isdescribed in the present specification may be a computing device such asa desktop or a laptop or a portable terminal such as a tablet PC or apersonal digital assistant (PDA).

Referring to FIG. 1 , a bone age estimation apparatus may extract aregion of interest including a cervical spine region from a lateralcephalometric radiographic image obtained by imaging a subject'scervical spine, by using a first deep learning model in step 100. In theexample embodiment, the region of interest may include at least one of asecond cervical spine, a third cervical spine, and a fourth cervicalspine of the subject. The bone age estimation apparatus may display alateral cephalometric radiographic image obtained by imaging a subject'scervical spine and the region of interest on one screen. Afterextracting the region of interest, the bone age estimation apparatus mayadjust a brightness of the region of interest and reverse the left andright of the region of interest.

The bone age estimation apparatus may extract landmarks from theextracted region of interest by using a second deep learning model instep 120. Here, the landmarks may be points obtained by anatomicallymeasuring and marking at least one of the second cervical spine, thethird cervical spine, and the fourth cervical spine of the subject. Thebone age estimation apparatus may extract thirteen landmarkscorresponding to a lower border edge of the second cervical spine, anupper border edge and a lower border edge of the third cervical spine,and an upper border edge and a lower border edge of the fourth cervicalspine from the region of interest using the second deep learning model.According to the example embodiment, the bone age estimation apparatusmay provide a function of confirming that the landmarks are notcompletely extracted so as not to move to another image. That is, thebone age estimation apparatus may provide a function of moving toanother image after extracting all thirteen landmarks. The bone ageestimation apparatus may adjust sizes of the landmarks. A trackingnumber is assigned to each of extracted landmarks on the basis of thepositions of the landmarks and the landmarks and the tracking numbers ofthe landmarks may be displayed in the region of interest. According tothe example embodiment, it is determined whether to display theextracted landmarks on the basis of a user input to select a landmark tobe displayed. That is, a user (or a medical specialist) may directlyselect landmarks to be displayed in the region of interest. For example,the user checks a checkbox to select a landmark to be displayed andundoes the checkbox to select a landmark which is not to be displayed.

When the bone age estimation apparatus receives a user input to changethe extracted landmarks, the bone age estimation apparatus may change acoordinate of the extracted landmarks on the basis of the user input.That is, the user may modify the positions of the landmarks with respectto the landmarks extracted by the bone age estimation apparatus asintended by the user. When the coordinates of the extracted landmarksare changed by the user input, the bone age estimation apparatus maytrain the second deep learning model on the basis of the changedcoordinate.

The bone age estimation apparatus may provide a numerical value obtainedby calculating a degree to which the extraction of the landmarks isperformed by a percentage, on the basis of a predetermined period, whileextracting the landmarks. For example, the bone age estimation apparatusvisually represents a progress that the extraction of the landmarks iscompleted by a percentage.

In step 130, the bone age estimation apparatus may calculate a landmarknumerical value on the basis of the extracted landmark. The bone ageestimation apparatus may calculate a landmark numerical value includingat least one of a ratio of a height to a length of the cervical spinelower border, a ratio of a height to a length of the cervical spinelower border that draws a vertical line, and a concavity ratio of thecervical spine lower border, on the basis of the coordinates of thelandmarks. The bone age estimation apparatus may calculate a ratio of aheight to the length of the cervical spine lower border and a ratio of aheight to the length of the cervical spine lower border which draws avertical line, for the third cervical spine and the fourth cervicalspine, and calculate a concavity ratio of the lower borders for thesecond cervical spine, the third cervical spine, and the fourth cervicalspine.

During the process of calculating the ratio of the height to the lengthof the cervical spine lower border, a length of the lower borderindicates a length of a line connecting front and rear peaks of thelower border and the height indicates an average value of two lengthsbetween the lower border and the upper border.

During the process of calculating the ratio of the height to the lengthof the cervical spine lower border which draws a vertical line, a lengthof the lower border indicates a length of a line connecting front andrear peaks of the lower border and the height may indicate an averagevalue of two straight distances between the lower border and the upperborder.

The concavity ratio of the lower border may be a ratio of c to w when cis calculated by a straight distance to the deepest point of the lowerborder from a line connecting the front and rear peaks of the lowerborder and the length of the lower border is calculated by a length w ofa line connecting the front and rear peaks of the lower border.

The bone age estimation apparatus may provide maturity information of amaturation stage of the cervical spine on the basis of the calculatedlandmark numerical value calculated in step 140. According to theexample embodiment, the bone age estimation apparatus may visuallyprovide the maturity information to the user.

The bone age estimation apparatus may visually provide the maturityinformation of the cervical spine on the basis of at least one of achronological age of the subject, a gender of the subject, age-specificstandardization information for a concavity of the fourth cervical spinelower border corresponding to the gender of the subject, and a concavityratio of the fourth cervical spine lower border of the subject. Here,the chronological age may be calculated by dividing one year into fourquarters.

The bone age estimation apparatus may provide at least one of a graphcorresponding to the standardization information, a graph correspondingto the concavity ratio of the fourth cervical spine lower border of thesubject, and a graph corresponding to the chronological age of thesubject to be overlaid. The bone age estimation apparatus may representthe standardization information for the concavity ratio of the fourthcervical spine lower border on the graph, represent the chronologicalage of the subject with a vertical line thereon and represent theconcavity ratio of the fourth cervical spine lower border with ahorizontal line. The user may confirm a standard growth for the age ofthe subject through the graph on which three information is overlaid.

FIGS. 2A to 2C are views illustrating examples of an interface in whicha bone age estimation method according to an example embodiment isperformed.

The bone age estimation apparatus may provide a cephalometric image anda cropped region of interest 220 including a cervical spine region 210of the patient in an interface of FIG. 2A. A region 210 cropped from thecephalometric image may be represented by a bounding box. The region 210represented by the bounding box and the cropped region of interest 220which is displayed on the right may be the same image.

The landmarks 240 extracted from an interface of FIG. 2B may bedisplayed on the region of interest. The interface of FIG. 2B mayinclude landmark numeric values 270 calculated on the basis ofcoordinate information of the landmarks. Further, the interface of FIG.2B may include a graph 230 representing a concavity ratio of the fourthcervical spine corresponding to the gender of the patient, a verticalgraph 250 representing a chronological age of the patient, and ahorizontal graph 260 representing a concavity ratio of the fourthcervical spine lower border. The bone age estimation apparatus providesthree graphs 230, 250, and 260 to be overlaid to allow the user tointuitively confirm the maturity information of the cervical spine ofthe patient. The user or the patient may confirm a maturity relative tothe standard growth level corresponding to the age of the patient.

An interface which is provided by the bone age estimation apparatus toperform a bone age estimation method may visually provide a numericalvalue obtained by calculating a degree that the extraction of thelandmarks is completed by a percentage. Further, the interface mayprovide a region in which a user input to adjust a brightness of theimage in which the region of interest is cropped may be received.

The bone age estimation apparatus assigns a tracking number on the basisof a position of each of the landmarks to be displayed through aninterface of FIG. 2C. According to the example embodiment, C2 LPcorresponding to a tracking number 1 denotes a left landmark of a lowerborder of the second cervical spine, C2 LM corresponding to a trackingnumber 2 denotes a middle landmark of the lower border of the secondcervical spine, and C2 LA corresponding to a tracking number 3 denotes aright landmark of the lower border of the second cervical spine. C3 UPcorresponding to a tracking number 4 denotes a left landmark of an upperborder of the third cervical spine, C3 UA corresponding to a trackingnumber 5 denotes a right landmark of the upper border of the thirdcervical spine, C3 LP corresponding to a tracking number 6 denotes aleft landmark of a lower border of the third cervical spine, C3 LMcorresponding to a tracking number 7 denotes a middle landmark of thelower border of the third cervical spine, and C3 LA corresponding to atracking number 8 denotes a right landmark of the lower border of thethird cervical spine. C4 UP corresponding to a tracking number 9 denotesa left landmark of an upper border of the fourth cervical spine, C4 UAcorresponding to a tracking number 10 denotes a right landmark of theupper border of the fourth cervical spine, C4 LP corresponding to atracking number 11 denotes a left landmark of a lower border of thefourth cervical spine, C4 LM corresponding to a tracking number 12denotes a middle landmark of the lower border of the fourth cervicalspine, and C4 LA corresponding to a tracking number 13 denotes a rightlandmark of the lower border of the fourth cervical spine. In thereference numeral 280, the user checks or undoes a check box for eachlandmark to determine whether to display the landmarks. Referring toFIG. 2C, the user undoes the check boxes corresponding to the trackingnumber 7 and the tracking number 8 so as not to display a seventhlandmark and an eighth landmark. The user may move the positions of thedisplayed landmarks at the user's discretion. Further, the user may alsoadjust the sizes of the displayed landmarks. According to the exampleembodiment, colors of the landmarks to be displayed may be determined onthe basis of the position. For example, landmarks corresponding to theleft of the lower border are displayed with red, landmarks correspondingto the middle of the lower border are displayed with green, landmarkscorresponding to the right of the lower border are displayed with blue,landmarks corresponding to the left of the upper border are displayedwith yellow, and landmarks corresponding to the right of the upperborder are displayed with sky blue.

FIG. 3 is a view illustrating a bone age estimation apparatus accordingto another example embodiment.

Referring to FIG. 3 , a bone age estimation apparatus 300 corresponds tothe bone age estimation apparatus described in the presentspecification. The bone age estimation apparatus 300 may include aprocessor 310, a memory 320, and a communication unit 330. Further, thebone age estimation apparatus 300 may further include a user inputinterface 340 and a display 350.

The memory 320 is connected to the processor 310 and stores instructionsexecutable by the processor 310, data to be operated by the processor310, or data processed by the processor 310. The memory 320 may includenon-transitory computer-readable media, such as high-speed random accessmemories and/or non-volatile computer-readable storage media (forexample, one or more disk storage devices, flash memory devices, orother non-volatile solid state memory devices).

The communication unit 330 provides an interface to communicate with anexternal device. For example, the communication unit 330 may receive alateral cephalometric radiographic image from the external device.

The display 350 may output an example of a screen that the bone ageestimation apparatus 300 executes an artificial intelligence basedradiographic image diagnosis assistance program.

Further, the user input interface 340 may receive a user input to changeextracted landmarks input by the user and a user input to select alandmark to be displayed.

The processor 310 may control the bone age estimation apparatus 300 toallow the bone age estimation apparatus 300 to perform one or moreoperations related to an operation of the bone age estimation apparatus300.

For example, the processor 310 may control the bone age estimationapparatus 300 to allow the bone age estimation apparatus 300 to extracta region of interest including a cervical spine region from a lateralcephalometric radiographic image obtained by imaging subject's cervicalspine by using a first deep learning model and extract landmarks fromthe extracted region of interest by using a second deep learning model.

The processor 310 may control the bone age estimation apparatus 300 toallow the bone age estimation apparatus 300 to calculate a landmarknumerical value on the basis of the extracted landmark. The processor310 may control the bone age estimation apparatus 300 to allow the boneage estimation apparatus 300 to calculate a landmark numerical valueincluding at least one of a ratio of a height to a length of thecervical spine lower border, a ratio of a height to a length of thecervical spine lower border that draws a vertical line, and a ratio of aconcavity of the cervical spine lower border, on the basis of thecoordinates of the landmarks. When the bone age estimation apparatus 300receives the user input to change the extracted landmarks, the processor310 may control the bone age estimation apparatus 300 to change thecoordinate of the extracted landmark on the basis of the user input.When the coordinate of the extracted landmark is changed by the userinput, the processor 310 may control the bone age estimation apparatus300 to train the second deep learning model on the basis of the changedcoordinate.

The bone age estimation apparatus 300 may be controlled to providematurity information of a maturation stage of the cervical spine on thebasis of the calculated landmark numerical value. The processor 310 maycontrol the bone age estimation apparatus 300 to allow the bone ageestimation apparatus 300 to visually provide the maturity information ofthe cervical spine on the basis of at least one of a chronological ageof the subject, a gender of the subject, age-specific standardizationinformation for a concavity of the fourth cervical spine lower bordercorresponding to the gender of the subject, and a concavity ratio of thefourth cervical spine lower border of the subject. The processor 310 maycontrol the bone age estimation apparatus 300 to allow the bone ageestimation apparatus 300 to provide at least one of a graphcorresponding to standardization information, a graph corresponding to aconcavity ratio of a fourth cervical spine lower border of the subject,and a graph corresponding to a chronological age of the subject to beoverlaid.

The method according to the example embodiment may be implemented as aprogram command which may be executed by various computers to berecorded in a computer readable medium. The computer readable medium mayinclude solely a program command, a data file, and a data structure or acombination thereof. The program instruction recorded in the medium maybe specifically designed or constructed for the example embodiment orknown to those skilled in the art of a computer software to be used.Examples of the computer readable recording medium include magneticmedia such as a hard disk, a floppy disk, or a magnetic tape, opticalmedia such as a CD-ROM or a DVD, magneto-optical media such as afloptical disk, and a hardware device which is specifically configuredto store and execute the program command such as a ROM, a RAM, and aflash memory. Examples of the program command include not only a machinelanguage code which is created by a compiler but also a high levellanguage code which may be executed by a computer using an interpreter.The hardware device may operate as one or more software modules in orderto perform the operation of the example embodiment and vice versa.

The software may include a computer program, a code, an instruction, ora combination of one or more of them and configure the processing deviceto be operated as desired or independently or collectively command theprocessing device. The software and/or data may be permanently ortemporarily embodied in an arbitrary type of machine, component,physical device, virtual equipment, computer storage medium, or device,or signal wave to be transmitted to be interpreted by a processingdevice or provide command or data to the processing device. The softwaremay be distributed on a computer system connected through a network tobe stored or executed in a distributed manner. The software and data maybe stored in one or more computer readable recording media.

As described above, although example embodiments have been described bylimited drawings, those skilled in the art may apply various technicalmodifications and changes based on the above description. For example,even when the above-described techniques are performed by differentorder from the described method and/or components such as systems,structures, devices, or circuits described above are coupled or combinedin a different manner from the described method or replaced orsubstituted with other components or equivalents, the appropriateresults can be achieved.

Therefore, other implements, other embodiments, and equivalents to theclaims are within the scope of the following claims.

1. A bone age estimation method, comprising the steps of: extracting aregion of interest including a cervical spine region from a lateralcephalometric radiographic image obtained by imaging a subject'scervical spine, by using a first deep learning model; extractinglandmarks from the extracted region of interest by using a second deeplearning model; calculating a landmark numerical value on the basis ofthe extracted landmarks; and providing maturity information of amaturation stage of the cervical spine on the basis of the calculatedlandmark numerical value.
 2. The bone age estimation method of claim 1,wherein the step of calculating a landmark numerical value includes: astep of calculating a landmark numerical value including at least one ofa ratio of a height to a length of the cervical spine lower border, aratio of a height to a length of the cervical spine lower border thatdraws a vertical line, and a concavity ratio of the cervical spine lowerborder, on the basis of the coordinates of the landmarks.
 3. The boneage estimation method of claim 1, wherein the step of extractinglandmarks includes: a step of changing a coordinate of the extractedlandmarks on the basis of the user input when a user input to change theextracted landmark is received.
 4. The bone age estimation method ofclaim 3, further comprising: a step of training the second deep learningmodel on the basis of the changed coordinate when the coordinate of theextracted landmark is changed by the user input.
 5. The bone ageestimation method of claim 1, wherein the step of providing maturityinformation includes: a step of visually providing the maturityinformation of the cervical spine on the basis of at least one of achronological age of the subject, a gender of the subject, age-specificstandardization information for a concavity of a fourth cervical spinelower border corresponding to the gender of the subject, and a concavityratio of the fourth cervical spine lower border of the subject.
 6. Thebone age estimation method of claim 5, wherein in the step of providingmaturity information, at least one of a graph corresponding to thestandardization information, a graph corresponding to the concavityratio of the fourth cervical spine lower border of the subject, and agraph corresponding to the chronological age of the subject is providedto be overlaid.
 7. The bone age estimation method of claim 1, furthercomprising: a step of adjusting a brightness of the region of interestand reversing left and right of the region of interest after extractingthe region of interest.
 8. The bone age estimation method of claim 1,wherein the step of extracting landmarks includes: a step of providing anumerical value obtained by calculating a progress degree that thelandmarks are extracted by a percentage, on the basis of a predeterminedperiod.
 9. The bone age estimation method of claim 1, wherein the regionof interest includes at least one of a second cervical spine, a thirdcervical spine, and a fourth cervical spine of the subject.
 10. The boneage estimation method of claim 1, wherein each of the extractedlandmarks is assigned with a tracking number on the basis of a positionof each of the landmarks, and the landmarks and the tracking numberscorresponding to the landmarks are displayed in the region of interest.11. The bone age estimation method of claim 10, wherein it is determinedwhether to display the extracted landmarks on the basis of the userinput to select a landmark to be displayed.
 12. The bone age estimationmethod of claim 9, wherein the landmarks are points obtained byanatomically measuring and marking at least one of the second cervicalspine, the third cervical spine, and the fourth cervical spine of thesubject.
 13. A non-transitory computer readable storage medium storinginstructions that are operable with a processor to execute the method ofclaim
 1. 14. A bone age estimation apparatus which performs a bone ageestimation method, the apparatus comprising: a memory; and a processor;wherein the memory stores instructions executable by the processor thatwhen executed by the processor, the processor controls the bone ageestimation apparatus to allow the bone age estimation apparatus toextract a region of interest including a cervical spine region from alateral cephalometric radiographic image obtained by imaging a subject'scervical spine, by using a first deep learning model, extract landmarksfrom the extracted region of interest by using a second deep learningmodel, calculate a landmark numerical value on the basis of theextracted landmarks, and provide maturity information of a maturationstage of the cervical spine on the basis of the calculated landmarknumerical value.
 15. The bone age estimation apparatus of claim 14,wherein the processor controls the bone age estimation apparatus toallow the bone age estimation apparatus to calculate a landmarknumerical value including at least one of a ratio of a height to alength of the cervical spine lower border, a ratio of a height to alength of the cervical spine lower border that draws a vertical line,and a concavity ratio of the cervical spine lower border, on the basisof the coordinates of the landmarks.
 16. The bone age estimationapparatus of claim 14, wherein the processor controls the bone ageestimation apparatus to allow the bone age estimation apparatus tochange a coordinate of the extracted landmarks on the basis of the userinput when a user input to change the extracted landmark is received.17. The bone age estimation apparatus of claim 16, wherein the processorcontrols the bone age estimation apparatus to allow the bone ageestimation apparatus to train the second deep learning model on thebasis of the changed coordinate when the coordinate of the extractedlandmark is changed by the user input.
 18. The bone age estimationapparatus of claim 14, wherein the processor controls the bone ageestimation apparatus to allow the bone age estimation apparatus tovisually provide the maturity information of the cervical spine on thebasis of at least one of a chronological age of the subject, a gender ofthe subject, age-specific standardization information for a concavity ofa fourth cervical spine lower border corresponding to the gender of thesubject, and a concavity ratio of the fourth cervical spine lower borderof the subject.
 19. The bone age estimation apparatus of claim 18,wherein the processor controls the bone age estimation apparatus toallow the bone age estimation apparatus to provide at least one of agraph corresponding to the standardization information, a graphcorresponding to the concavity ratio of the fourth cervical spine lowerborder of the subject, and a graph corresponding to the chronologicalage of the subject to be overlaid.
 20. The bone age estimation apparatusof claim 14, wherein the landmarks are points obtained by anatomicallymeasuring and marking at least one of a second cervical spine, a thirdcervical spine, and a fourth cervical spine of the subject.